Download Single-cell recordings in the human medial temporal lobe

J. Anat. (2014)
REVIEW
Single-cell recordings in the human medial temporal
lobe
Hernan G. Rey,1 Matias J. Ison,1,2 Carlos Pedreira,1 Antonio Valentin,3,4 Gonzalo Alarcon,3,4,5
Richard Selway,6 Mark P. Richardson3,7 and Rodrigo Quian Quiroga1,2
1
Centre for Systems Neuroscience, University of Leicester, Leicester, UK
Department of Engineering, University of Leicester, Leicester, UK
3
Department of Clinical Neuroscience, King’s College London, London, UK
4
Department of Clinical Neurophysiology, King’s College Hospital, London, UK
5
Departamento de Fisiologıa, Universidad Complutense, Madrid, Spain
6
Department of Neurosurgery, King’s College Hospital, London, UK
7
Department of Neurology, King’s College Hospital, London, UK
It has long been established that the ventral visual pathway
has an important role in visual perception and recognition
(Logothetis & Sheinberg, 1996; Tanaka, 1996; Tsao & Livingstone, 2008). As we move forward along the pathway, we
go from neurons in V1 responding to very low-level
features of the image sensed by the retina (e.g. showing
selectivity to local orientations; Hubel & Wiesel, 1962), to
neurons in the infero-temporal cortex (ITC) responding to
high-level features of the image (e.g. faces; Gross, 2008).
From ITC there are direct projections to the medial
temporal lobe (MTL; Saleem & Tanaka, 1996; Suzuki, 1996),
a brain region that includes the hippocampus, the
amygdala, and entorhinal, parahippocampal and perirhinal
Correspondence
Rodrigo Quian Quiroga, Centre for Systems Neuroscience, University
of Leicester, Leicester, UK. E: [email protected]
Accepted for publication 11 July 2014
2
cortices, structures exhibiting complex connectivity patterns
between them and with the neocortex (Suzuki, 1996;
Burwell & Agster, 2008).
There is a large amount of evidence supporting a key role
of the MTL in declarative memory, from studies in animals
to evaluations of patients with lesions (Squire & Zola-Morgan, 1991; Eichenbaum, 2001; Squire et al. 2004; Moscovitch et al. 2005). In order to study such memory processes,
human subjects appear to be the ideal model, given the
large amount of rich experiences they can recall and report.
However, the study of the human brain has been mainly
carried out using non-invasive techniques, such as
functional magnetic resonance imaging (fMRI) and (scalp)
electroencephalogram (EEG). The main caveat of these
techniques is that they give very limited and indirect information of the neural processes underlying brain functions.
Ideally, one would like to study the activity of single cells
directly, but single-cell studies in humans are very limited
due to obvious ethical reasons. Nevertheless, patients
suffering certain disorders, such as Parkinson’s disease,
dystonia or epilepsy, might require invasive recordings in
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
This is an open access article under the terms of the Creative Commons Attribution License, which permits use,
distribution and reproduction in any medium, provided the original work is properly cited.
PE: Jegadeesh
No. of pages: 15
Dispatch: 1.8.14
12228
Introduction
Manuscript No.
Recordings from individual neurons in patients who are implanted with depth electrodes for clinical reasons
have opened the possibility to narrow down the gap between neurophysiological studies in animals and noninvasive (e.g. functional magnetic resonance imaging, electroencephalogram, magnetoencephalography)
investigations in humans. Here we provide a description of the main procedures for electrode implantation and
recordings, the experimental paradigms used and the main steps for processing the data. We also present key
characteristics of the so-called ‘concept cells’, neurons in the human medial temporal lobe with selective and
invariant responses that represent the meaning of the stimulus, and discuss their proposed role in declarative
memory. Finally, we present novel results dealing with the stability of the representation given by these
neurons, by studying the effect of stimulus repetition in the strength of the responses. In particular, we show
that, after an initial decay, the response strength reaches an asymptotic value after approximately 15
presentations that remains above baseline for the whole duration of the experiment.
3 Key words: xxxx; xxxx; xxxx.
J O A
Abstract
CE: Parameshwari S.
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doi: 10.1111/joa.12228
2 Human medial temporal lobe, H. G. Rey et al.
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order to obtain an accurate diagnosis and/or appropriate
treatment (Engel et al. 2005). This allows intracranial
recordings in the MTL in humans, given that this area is
involved in certain forms of epilepsy (Niedermeyer, 1993).
Moreover, with the proper setup, it is possible to record not
only intracranial EEG or local field potentials (LFPs) – which
account for mean activation patterns across several neurons
– but also the electrical activity of individual neurons (Quian
Quiroga & Panzeri, 2009).
In the late 1970s, the first studies on single-cell activity in
the human MTL during memory tasks took place (Halgren
et al. 1978). In the following years, several experiments to
study different aspects of human cognition – including language, memory, perception, emotion – were developed
(Fried et al. 1997; Ojemann et al. 1988, 1998; see Mukamel
& Fried, 2012 for a review) and in 2000, the presence of category-specific neurons (some responding only to faces, others to objects, others to animals, etc.) in the MTL was
reported (Kreiman et al. 2000a). Following these findings,
neurons in the MTL with highly selective and invariant
responses were found (Quian Quiroga et al. 2005), and it
was argued that these so-called ‘concept cells’ or ‘Jennifer
Aniston neurons’ represent the meaning of the stimulus for
declarative memory functions (Quian Quiroga et al. 2008a;
Quian Quiroga, 2012a).
In this article we present different aspects related to the
recording and behavior of ‘concept cells’ in the human
MTL. We cover details of the surgical procedure and data
acquisition, the experimental paradigms to study these neurons, the data processing necessary to analyze the neural
responses, and discuss several properties of these cells that
help understanding their role. We also present novel results
dealing with the stability of the responses of concept cells,
using a visual paradigm where each stimulus was shown
between 20 and 30 times. This allowed us to study the modulation of the responses by stimulus repetition across several trials. We found that the strength of neuronal
responses decreased until reaching a stable value above
baseline firing levels.
Surgical procedure and recordings
In about 25% of the patients suffering from epilepsy, antiepileptic drugs fail to control the seizures, what is known
€ ders, 2008; Kwan et al.
as refractory epilepsy (Schuele & Lu
2011). Based on the results of several non-invasive tests
(including scalp EEG, structural imaging and neuropsychological assessments) these patients might be candidates for
a surgical solution, in which the epileptic focus is resected.
However, before taking such a measure, it is clearly critical
to have a reasonable degree of confidence about the location of the seizure focus. In some of these cases, non-invasive methods might not be sufficient to unambiguously
localize the seizure onset zone, and intracranial recordings
are required. This technique started in the 1930s and, at the
1
time, intracranial recordings were performed only during
surgery. Over the following decades, the technique for
invasive recordings improved considerably (see Engel et al.
2005 for a timeline on the history of invasive recordings in
the human brain). Nowadays, the subacute implantation of
intracranial electrodes for an extended period allows continuous monitoring of the electrical activity in areas where
the epileptic focus is presumed to be potentially located.
Recordings are taken from several target areas and, in general, only a few of these sites end up being involved in the
epileptogenesis (Mukamel & Fried, 2012). Depending on
the rate of spontaneous seizures and the amount of data
collected to localize the epileptic focus, patients might
remain monitored from a few days to a couple of weeks.
The choice of the type of electrodes to be used depends
on the location of the potential epileptic focus (or foci). A
widely used type of intracranial electrodes is the subdural
one, including strips and grids, consisting of an array of circular electrodes arranged along a line or a surface, respectively, and placed on top of the cortex. Grids usually require
a full craniotomy for their implantation and they are particularly suitable for functional mapping of the cortex, as they
provide a stable and dense array of recording electrodes. In
areas responsible for language, motor or sensory function,
the brain regions that must not be removed can be identified with electrical stimulation of the different grid contacts
(Penfield, 1975). The advantage of strips is their larger flexibility for placement, for example along a cortical gyrus, and
the fact that they can be inserted through a single burr hole
using different trajectories.
Nonetheless, in several cases the focus might be located
in areas of the cortex that are difficult to be accessed with
surface electrodes (orbitofrontal, cingulate, etc.) or subcortically, as in the case of the MTL. To target such areas the
use of depth electrodes is often preferred. They are composed of a flexible polyurethane probe (Fig. 1A) with low
impedance platinum-iridium contacts that record intracranial EEG signals and, particularly, characteristic epileptogenic discharges or seizures. Their placement can be very
accurate due to the use of stereotactic frames. In fact, preoperative MRI scans are typically used to establish the target
position of the electrodes, based on clinical criteria, and the
corresponding coordinates in the frame (Fig. 1B). After surgery, a postoperative scan can be co-registered with the
planning to verify the actual position of the electrodes
(Fig. 1C).
In the 1950s, the first single-units recordings from the
human cortex were performed during surgery for epilepsy
(Ward & Thomas, 1955). Single-cell recordings over
extended periods of time were introduced in the early
1970s at the University California Los Angeles (UCLA) while
patients remained in the hospital ward for seizure monitoring (Babb et al. 1973). These recordings were developed
further at UCLA and nowadays they are performed by
inserting platinum-iridium microwires (with a 40 lm width)
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
Human medial temporal lobe, H. G. Rey et al. 3
a
Macro
electrodes
Micro
} electrodes
~4 mm
b
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filtered between 0.1 and 9000 Hz, and sampled at 32 556
Hz. The other two patients were recorded using a 64-channel Neuroport system (Blackrock Microsystems), with the
differential signal from each channel filtered between 0.3
and 7500 Hz, and sampled at 30 000 Hz. The data were processed offline, and the high-frequency activity (> 300 Hz)
was extracted to identify the firing of the neurons (see Figs
3a and 4a for examples).
Experimental paradigms
c
Fig. 1 Surgical planning. (A) Before implantation, the microwires are
inserted in the lumen of the polyurethane probe so they can be
trimmed to the desired length (~4 mm). (B) Based on a pre-operative
MRI scan, computer software provides the stereotactic coordinates of
the targets where the electrodes should be implanted. (C) After surgery, a post-operative MRI or CT is co-registered with the preoperative
MRI to assess the accuracy of the actual position of the electrodes.
inside the lumen of the probe containing the depth iEEG
electrodes, which protrude 3–5 mm from the tip of the
probe (Fig. 1A; Fried et al. 1997). The microwires have high
impedance (300–1000 kΩ) and are suitable for recording
action potentials and LFPs.
The data we present in this study, both for illustrating single-cell responses and for studying the stability of these
responses, were collected using commercially available (AdTech) depth electrodes, where each bundle had eight active
microwires and one low-impedance microwire that was
used as local reference. All studies were performed at King’s
College Hospital in London (UK) and were approved by its
Research Ethics Committee. The electrodes were implanted
bilaterally in the hippocampus and amygdala. Two patients
were recorded using a 64-channel Digital Lynx system
(Neuralynx), with the differential signal from each channel
The prolonged nature of the monitoring of epileptic
patients with intracranial recordings, in order to record a
sufficient number of spontaneous seizures, provides a
unique opportunity to record neural activity for several days
while the patients perform different cognitive tasks. Experiments with conscious human subjects have several advantages over analogous experiments with animals as, in
particular, humans can be easily instructed to perform complex tasks, thus avoiding overtraining. Moreover, they can
deliver detailed feedback and they can be tested on their
unique abilities, such as language (Ojemann et al. 1988) or
verbal recall (Gelbard-Sagiv et al. 2008). Also, when studying human cognition, invasive recordings provide several
advantages against non-invasive ones, such as an improvement in both temporal and spatial resolution, high signal
to noise ratio and, particularly, the unique possibility of
recording directly the activity of single neurons.
In general, intracranial recordings have been helpful to
improve our understanding in many topics in cognitive neuroscience (Mukamel & Fried, 2012). In this work we will
focus on the behavior and function of ‘concept cells’ found
in the human MTL (Quian Quiroga, 2012a). These neurons
have very selective responses triggered by only a few stimuli. In order to find such stimuli, a simple visual task is used
(Fig. 2), in which the subject is sat facing a laptop computer
where a set of about 100 stimuli are presented for 1 s, six
times each in pseudorandom order (Quian Quiroga et al.
2005). To make sure subjects pay attention to the stimuli,
after the picture presentation they have to respond
whether or not there was a person in the picture by pressing different arrow keys. These ‘screening sessions’ typically
last about half an hour, and they are conducted in order to
determine which neurons are responsive and to which set
of pictures. The set of pictures used includes items well
familiar to the patient, like images of celebrities, landmarks,
animals, and the patient’s relatives and friends. This set of
pictures is actually tuned for each patient according to their
preferences and background, as it has been shown that
MTL neurons respond preferentially to personally relevant
images (Viskontas et al. 2009).
Once it has been identified which picture/s triggers the
firing of which neuron, different experimental paradigms
are performed shortly after in order to test the firing patterns of these neurons under different tasks and conditions.
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
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4 Human medial temporal lobe, H. G. Rey et al.
500 ms
1
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Response time
Stimulus
Person?
+
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...
Screening session: ~100 pictures, 6 trials each
...
Testing session: 10 to 25 pictures, 25 to 35 trials each
Fig. 2 Example of screening and testing sessions. (Top) Each trial
started with a fixation cross on the screen for 500 ms, followed by a
picture displayed for 1000 ms. Next, the screen went black, and the
patient had to press a key to respond whether or not there was a person in the picture. Finally, there was a random inter-trial interval
between 600 and 800 ms. (Middle) A screening session is performed
by showing a set of ~100 pictures of animals, celebrities and landmarks, with each picture presented six times. (Bottom) The testing session is performed within a few hours after the screening session. In
this case, a set of pictures eliciting responses in the screening session
is shown again many more times to study the stability of the
responses (see Fig. 8).
The data shown in Figs 3–6 were obtained this way by
presenting many repetitions of the stimuli eliciting
responses in the screening sessions. In the first study describing concept cells (Quian Quiroga et al. 2005), different pictures of the persons/objects eliciting responses in the
screening sessions were shown to the subjects in the following sessions. This allowed establishing a very high degree of
visual invariance of these responses, as the neurons tended
to fire selectively to the different pictures of the same person and even to the person’s name written on the screen. A
following study showed that such a degree of invariance
extended to other sensory modalities, as these neurons also
fired selectively when the name of the person eliciting a
response (and not other names) was pronounced by a computer-synthetized voice (Quian Quiroga et al. 2009). Besides
the interest of the results obtained from the screening sessions per se – to characterize the neurons’ selectivity (Waydo et al. 2006; Quian Quiroga et al. 2007), strength and
latency of responses (Mormann et al. 2008; Quian Quiroga
et al. 2009), preferential tuning (Viskontas et al. 2009; Mormann et al. 2011), correlation with LFP responses (Kraskov
et al. 2007), repetition suppression effects (Pedreira et al.
2010), differential firing of pyramidal cells and interneurons
(Ison et al. 2011), etc. – other experiments that explicitly
used the knowledge of pictures triggering responses to
study different properties of these neurons include a
paradigm showing these pictures at the threshold of conscious perception (Quian Quiroga et al. 2008b), in a change
blindness experiment (Reddy et al. 2006), another paradigm
studying these neurons’ responses during free recall (Gelbard-Sagiv et al. 2008), another one assessing how these
neurons rapidly encode new associations (Ison et al. 2010),
and an experiment where the patients could voluntary control by internal thought which image from a pair (for which
there were neurons responding) was displayed in a computer screen (Cerf et al. 2010).
The importance of spike sorting
Action potentials (spikes) detected from implanted microwires might come from several neurons close enough to the
electrode tip (Martinez et al. 2009; Quian Quiroga & Panzeri, 2009). The first step to process these recordings is to
detect the spikes in the background noise (mainly generated by the activity of neurons further away), and to determine which spike corresponds to which neuron based on
the spike shapes, a process called spike sorting (Quian Quiroga, 2012b). For the data presented here, spike detection
and sorting was performed using Wave_Clus (Quian Quiroga et al. 2004). Briefly, the steps typically involved in spike
detection and sorting are: (i) band-pass filtering the data,
for example between 300 and 3000 Hz, using a zero-phase
filter to avoid distortion in the spike waveforms (Quian Quiroga, 2009); (ii) computation of a detection threshold based
on a robust estimate of the noise statistics; (iii) feature
extraction of the spike shapes, in the case of Wave_Clus
using the wavelet transform; and (iv) clustering of the
waveforms to identify the firing of the different units.
The importance of spike sorting is illustrated with the
example of Fig. 3. Figure 3a shows the result of the sorting
of the spikes recorded by an electrode in the left hippocampus. Throughout the 16-min recording, a total of 17 439
spikes was detected. However, these spikes belong to different units. In particular, clusters 3 and 5 are associated to single units that account for only 4% and 1% of the detected
spikes, respectively. As shown in Fig. 3b, the unit in cluster 3
responds strongly to the picture of Vladimir Putin, with
almost 20% of the spikes from this unit appearing between
500 and 1500 ms from the onset of this picture (representing 5% of the duration of the session). Still, a clear response
can be seen in the multiunit activity (i.e. just the detected
spikes, without sorting). On the contrary, the response to
the Taj Mahal from the unit in cluster 5, which also contains
about 20% of the spikes from this unit, is not observed in
the multiunit activity without the appropriate sorting. This
is a common finding with these recordings: the fact that
responses from sparsely firing or nearly silent neurons can
be missed without optimal spike sorting (Quian Quiroga,
2012a; see also next section).
Another example of a ‘silent neuron’ is shown in Fig. 4,
where cluster 3, which accounts for 2% of the spikes
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
Human medial temporal lobe, H. G. Rey et al. 5
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Detected
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Fig. 3 Single-unit responses. (a) Spike sorting. The top plot shows 60 s of the high-pass filtered recording (300–3000 Hz) from a microwire
implanted in the left anterior hippocampus (the red solid line is the detection threshold). The bottom part shows all the detected spikes (left) and
five clusters that were identified after sorting the spikes. (b) Raster plots associated to five pictures used during the session (first trial on top; time
zero corresponds to stimulus onset). When all the detected spikes are considered (first row), only the picture of Vladimir Putin elicited a clear
response. However, the response is actually elicited by the single unit associated to cluster 3. Moreover, the raster plots for cluster 5 allow us to
unravel a response to the picture of the Taj Mahal (that was not evident from the detected spikes). This cell fired selectively to this particular stimulus, remaining nearly silent to all the other stimuli.
detected in this channel, is associated to a single unit
selectively responding to a picture of George Harrison (this
response accounts for 15% of the spikes from this unit, representing 3% of the duration of the session). As before, it
would have been impossible to identify this response from
the detected spikes without optimal spike sorting. Figure 4
shows also a multiunit cluster (cluster 1; i.e. a cluster where,
due to low signal to noise ratio, it was not possible to separate the contribution of different units) that exhibits
responses to a picture of the patient himself and to one of
his daughter.
Characterization of MTL single-cell responses
In the previous section we have illustrated some of the main
properties of single-cell responses in the human MTL, particularly how they can selectively increase their firing from a
very low baseline activity. As discussed above, the detection
of these responses is challenging, first, due to the importance of optimal spike sorting and, second, due to the high
selectivity of the responses and the need to identify which
pictures should be used to trigger the neurons’ firing. The
use of screening sessions together with optimal data
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
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6 Human medial temporal lobe, H. G. Rey et al.
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Fig. 4 Multi and single unit responses. (a) Same conventions as in Fig. 3. In this case, four clusters were identified after sorting the spikes
recorded from a microwire implanted in the left anterior hippocampus. (b) Cluster 1 is associated to multi-unit activity showing responses to the
pictures of the patient and his daughter (pictures covered for confidentiality issues). In addition, cluster 3 is an example of another silent neuron,
in this case one responding selectively to the picture of George Harrison.
processing of the data were indeed crucial to the finding of
concept cells (Quian Quiroga et al. 2005). The most popular
of these neurons, for example, responded to seven different
pictures of Jennifer Aniston but not to 80 pictures of other
celebrities, landmarks, animals, etc. The fact that these neurons fire to the concept represented by the stimulus and
not to specific visual details, which could be common to a
particular subset of pictures, is reinforced by the fact that
the pictures of the same person used to test visual invariance included different backgrounds, poses, clothes, etc.
More conclusive evidence of such conceptual representation
is given by the fact that these neurons also fired to the
written and spoken name of the person (or object) eliciting
responses (Quian Quiroga et al. 2009).
Selectivity
An important characteristic of these MTL neural responses
is their high degree selectivity, with units responding on
average only to 1–3% of the presented pictures (Quian Quiroga et al. 2007). In Fig. 5, we show a characteristic example
of a single unit in the hippocampus with a very selective
response to a picture of Stonehenge. In fact, this sparsely
firing neuron (0.5 Hz on average) fired 45% of the spikes
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
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Fig. 5 Selectivity of single-cell responses. Responses of a single unit in the left hippocampus during a testing session, where 12 pictures were presented 35 times each (19 min recording). The dashed vertical lines in the histogram denote onset and offset of the response based on the automatic response criterion. This ‘silent’ neuron (0.5 Hz on average during the session) shows a very selective response to the picture of Stonehenge
(45% of the spikes took place in the trials associated to the Stonehenge picture, with half of them appearing between the onset and offset of the
response).
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Fig. 6 Stimulus relevance. (a) This unit in the left anterior hippocampus responded selectively to the pictures of Prince William, the Duchess of
Cambridge (Kate Middleton) and both of them together, in a recording that took place 4 weeks before the ‘Royal Wedding’. (b) This unit in the
left posterior hippocampus shows a selective response to the picture of Alastair Cook, a cricket player who was the ‘Player of the Ashes Series’
between England and Australia, which finished 2 months before the date of the recording.
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
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recorded in the session in response to the picture of Stonehenge, while the rest occurred on the other 11 pictures or
between trials.
Given the selectivity of these responses, the firing of these
neurons was used to decode the identity of the person
being shown, but it was in general not possible to decode
which picture of the person was presented each time
(Quian Quiroga et al. 2007). Therefore, these neurons give
an explicit representation of the meaning of the stimuli (i.e.
a neuron tells who the person being shown is).
Because these neurons fire to relatively very few stimuli,
each stimulus is likely to be encoded by a relatively small
neuronal assembly (Quian Quiroga et al. 2008a). In this
‘sparse coding’ view, the members of the assembly respond
in an explicit manner to the specific concept they encode
and typically remain silent for other objects. The extreme
case would be a one-to-one correspondence between a single neuron and a particular concept, leading to the socalled ‘grandmother cells’ (Quian Quiroga et al. 2008a,
2013). But there are several reasons to reject the hypothesis
of having one single cell per concept. Based on the total
number of recorded neurons during a session, the number
of stimuli presented and the number of responsive units,
it has been estimated that out of about 109 MTL neurons,
< 106 would be involved in representing a given concept
(Waydo et al. 2006). Moreover, each MTL neuron was
estimated to be involved in encoding between 50 and 150
distinct individuals or objects. As a matter of fact, these
estimates should only be considered as upper bounds, given
that: (i) the images used were personally relevant to the
subject, thus increasing the likelihood of eliciting a
response; (ii) the relatively low number of pictures used
(about 100) limits the lowest selectivity value to 1%; and
(iii) it is difficult to detect very selective neurons firing to
even fewer pictures, which are likely not presented in the
~30-min recording sessions.
With respect to the number of concepts encoded by these
neurons, even though many responsive neurons fired to a
single concept, it cannot be ruled out that they could have
responded to other concepts if more pictures were shown.
In fact, it is not uncommon to find units that do fire to
more than one concept (Quian Quiroga et al. 2005, 2009).
Such cases include responses to Jennifer Aniston and Lisa
Kudrow (actresses of the series ‘Friends’), the Pisa and Eiffel
towers (Quian Quiroga et al. 2005), Luke Skywalker and
Yoda (Quian Quiroga et al. 2009), and Figs 4 and 6a in this
work. At the same time, neighboring neurons (e.g. units
recorded in the same channel and separated by performing
spike sorting) are typically involved in encoding completely
unrelated concepts (see Fig. 3; Quian Quiroga et al. 2009),
which suggests a non-topographic organization.
It is also interesting to consider how such a sparse and
conceptual representation may arise from the activity of
upstream cortical areas. First of all, on the basis of their
spike widths and firing rates it is in principle possible to
1
classify neurons as interneurons or pyramidal cells. Using
this classification, it was reported that MTL interneurons
tend to fire to a larger number of stimuli, a finding that
suggests a winner-takes-all mechanism that (by suppressing
the firing of other neurons) may generate the very selective
responses of pyramidal cells (Ison et al. 2011). Furthermore,
by imposing a sparse representation in a detailed network
model of visual processing, it was found that, without
external supervision, the network learnt to discriminate persons or objects even if shown in different ways, as found in
the human MTL (Waydo & Koch, 2008).
Latency of MTL responses
From the examples we have already presented, it can be
seen that the MTL responses have a very precise onset, typically between 200 and 600 ms after stimulus onset, with a
mean latency of about 300 ms (Mormann et al. 2008). The
timing of neural responses along the ventral visual pathway
is given by direct feedforward projections (Thorpe & FabreThorpe, 2001), culminating in responses at about 100 ms in
the ITC in monkeys (Kreiman et al. 2006; see also table 1 in
Mormann et al. 2008) and analogous structures in humans
(Vanrullen & Thorpe, 2001; Liu et al. 2009). From ITC there
are direct projections to the MTL (Saleem & Tanaka, 1996),
but there is a gap of at least 100 ms between the responses
in these two areas. A recent study (Rey et al. 2014) suggests
that the timing of MTL single-cell responses may be given
by LFP-evoked responses observed in this area. In particular,
it was shown that single-neuron responses in the MTL were
preceded by a global LFP deflection in the theta range (4–8
Hz), with the neurons’ firing locked to specific phases of
the LFP (Rey et al. 2014). This theta phase locking was proposed to reflect a global activation that provides a temporal
window for the processing of consciously perceived stimuli
in the MTL. The timing mechanism given by the theta LFP
responses may indeed be critical for synchronizing and combining multisensory information involving different processing times. The latency gap between ITC and MTL responses
would then reflect the further processing of sensory stimuli
(and perhaps the additional involvement of other areas,
such as the prefrontal cortex) in order to create a unified
conceptual representation that is used by the MTL for memory functions.
Relationship with conscious perception
Studies using flash-suppression or very short presentations
coupled with backward masking showed that the activity of
these MTL neurons does not follow the retinal input but
rather the subjective meaning of the perceived stimulus
(Kreiman et al. 2002; Quian Quiroga et al. 2008b). In particular, it was shown that these neurons fired only when the
subject recognized the stimulus eliciting responses and
remained silent when it was not recognized, even if the
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
Human medial temporal lobe, H. G. Rey et al. 9
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visual stimuli were identical (i.e. the same picture at the
same duration; Quian Quiroga et al. 2008b). In line with
this evidence, the firing of these MTL neurons to the picture
eliciting responses was higher when the subjects correctly
identified a change in a change detection paradigm, compared with when they missed it (Reddy et al. 2006).
Given the vast amount of converging evidence supporting the role of the MTL in declarative memory processes
but not in visual perception (Squire & Zola-Morgan, 1991;
Eichenbaum, 2001; Squire et al. 2004; Moscovitch et al.
2005), it should be noted that this relationship between single-unit responses and stimulus recognition does not imply
an involvement of the MTL in conscious visual perception.
On the contrary, and as argued below, it likely reflects the
subject’s awareness of the particular concepts being displayed for declarative, and particularly episodic, memory
functions, like later remembering having seen a picture of
Jennifer Aniston in the hospital ward (Quian Quiroga et al.
2008a).
Internally generated responses
Another feature of these MTL responses is that they can be
internally generated in the absence of the physical stimulus.
When subjects imagined pictures that previously triggered
the firing of a neuron (i.e. without the actual visual stimulus
being shown), it was possible to see the same selective
response as when viewing the picture (Kreiman et al.
2000b). Furthermore, Gelbard-Sagiv et al. (2008) found
selective single-cell responses during the presentation of
short video clips. Interestingly, they also found that when
the subjects were asked to describe the various video clips
that were presented (i.e. during free recall, in the absence
of any sensory input), the same neurons that were active
during stimulus encoding were reactivated during successful retrieval. The explicit representation of these MTL neurons was also exploited to achieve a voluntary modulation
of the responses (Cerf et al. 2010): A target image was presented at the beginning of the trial, and from a 50 to 50%
hybrid picture, the subjects could make a target picture
clearer, fading out the other one, by voluntarily modifying
the firing of the responsive MTL neurons. These results
highlight the power of internal representation to override
sensory input.
Putative function of concept cells
As information progresses through the ventral visual pathway, it gets transformed from a detailed representation
given by the receptive fields in the retina to a more abstract
and conceptual representation in the MTL, where a large
amount of details from low-level features are left behind.
As discussed above, considering the overwhelming evidence
linking the MTL to memory functions (Squire & Zola-Morgan, 1991; Eichenbaum, 2001; Squire et al. 2004;
Moscovitch et al. 2005), it has been postulated that concept
cells represent the meaning of the stimulus for declarative,
and particularly episodic, memory functions (Quian Quiroga
et al. 2008a; Quian Quiroga, 2012a). Representing the
meaning of the stimulus involves a high degree of invariance, in the sense that these neurons respond to the particular concept being presented and disregard the varying
details constituting the specific sensory representations. As
also discussed above, these neurons do not act in isolation
and a given concept is represented by the firing of a corresponding cell assembly. Sensory stimuli related to this concept, such as a picture or the written or spoken name of a
person, would then activate different subsets of the assembly, which in turn activate the whole assembly via pattern
completion (Quian Quiroga, 2012a).
A key mechanism related to episodic memories is the fast
creation of new associations, like remembering meeting a
person in a particular place. In this respect, it has been
shown that MTL neurons can form new invariant responses
and associations relatively quickly, for example, by responding to researchers performing experiments that were previously unknown to the patient (Quian Quiroga et al. 2009).
Supporting this view of concept cells related to memory,
patients with MTL lesions are impaired at accessing and
combining contextual information and at providing
detailed accounts of past experiences (Moscovitch et al.
2005) or even imagining new situations (Hassabis et al.
2007). Moreover, concept cells fired even in the absence of
physical stimuli, triggered by internal thought or recall. A
recent study in humans has also strengthened the link
between MTL and memory function by showing that the
phase locking between spiking and theta activity in this
area during encoding predicted memory success (Rutishauser et al. 2010).
The high degree of visual invariance found in the human
MTL has not been matched in other animals. In monkeys,
the ITC shows a distributed code and limited robustness to
basic image transformations (Logothetis & Sheinberg, 1996;
Tanaka, 1996; Hung et al. 2005; Kreiman et al. 2006). Still, it
is likely that, at least up to some degree, similar conceptual
representations may exist in other species. Face-selective
brain regions in the temporal lobe of monkeys, also known
as face patches, have shown different tuning properties
(Freiwald & Tsao, 2010). In particular, cells in the anterior
medial patch show sparse, individual-specific, view-invariant
response patterns. As this neural population approaches a
view-invariant representation of facial identity, they serve
as the closest experimental finding to concept cells in
humans. In addition, concept cells share many features with
place cells in the rodent hippocampus (Quian Quiroga,
2012a). Place cells increase their firing rate when the animal
crosses a specific location in an environment (O’keefe
& Nadel, 1978). They are selective, firing strongly to a
particular place field, and they show fast learning, with its
tuning being able to be formed within minutes (Wilson &
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
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McNaughton, 1993). They also show a non-topographic
organization, in the sense that neighboring place fields do
not correspond to nearby neurons (Redish et al. 2001).
Moreover, as concept cells, place cells can fire even without
visual information, as they maintain their tuning after the
light is turned off, i.e. without visual information (Quirk
et al. 1990). In line, it has been suggested that rather than
just encoding a ‘cognitive map’, place cells may be part of a
more general ‘memory space’ by encoding the common
features that link different episodes, particularly linking episodes that occur at the same location (Eichenbaum et al.
1999; Eichenbaum, 2004).
Stimulus relevance
Given the proposed role of concept cells in declarative
memory, in the following we focus on two characteristics of
these neurons that support this interpretation. First, if MTL
neurons are involved in extracting the meaning of a stimulus for memory functions, it is likely that, due to the inherently subjective nature of such an operation, it would
depend on the relevance and connotation that the stimulus
has for the subject (Quian Quiroga, 2012a). In line, it has
been shown that pictures of family members, including pictures of the patients themselves (see Fig. 4b), and pictures
of the researchers performing experiments with the
patients, who are in close contact with them throughout
their stay in hospital, are the most likely to elicit responses,
followed by pictures of celebrities, and then by pictures of
unfamiliar faces (Viskontas et al. 2009). In other words, we
can infer that the more personally relevant the stimulus,
the larger the number of neurons encoding it. This finding
is consistent with the larger extent of episodic recollections
and associations with other related concepts that personally
relevant images generate.
Stimulus relevance is given by the subject’s own interest
but also by the specific circumstances of what may be relevant at a particular time. To illustrate this, Fig. 6a shows an
example of a responsive unit in the anterior hippocampus
that fired to the pictures of Prince William, the Duchess of
Cambridge (Kate Middleton) and both of them together.
Although the patient later stated being not particularly
fond of matters related to the Royal family, the royal wedding took place about a month after the recording was performed and, being all over the media, this fact was
therefore likely to be within the network of concepts and
associations being salient for the subject at the time. We
can also speculate that for the subject such an event might
have later lost the relevance it had at the time, probably
affecting the size of the cell assembly encoding these concepts, and the remembered associations and episodes
linked to them. Another example comes from a unit in the
posterior hippocampus (Fig. 6b), which responded selectively to a picture of England’s cricket player Alastair Cook.
This recording was performed 2 months after a series of test
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matches between England and Australia, in which Cook
excelled and commanded the English team to victory.
The tendency of concept cells to preferentially encode
items that are relevant at the time the experiments are performed is reinforced by the fact that, although the
researchers that performed experiments were completely
unknown to the patients before the experiments took
place, they have a very frequent interaction with the
patients while these are being monitored with intracranial
recordings, and their pictures and the ones of family members had the largest likelihood to elicit responses. This likelihood was followed by pictures of celebrities and finally by
pictures of unknown faces (Viskontas et al. 2009). Naturally,
family members are associated to remote (and/or recent)
personal memories, while the experimenters are only associated to recent ones. This, in turn, raises the issue of whether
MTL neurons are just involved in consolidation of new
memories into cortex or whether they show a more stable
representation even after consolidation, with responses
decaying at a relatively slower rate given that episodes and
associations related to specific concepts become less relevant and are forgotten with time. In the next section we
will address this issue by studying variations of response
strength with stimulus repetition.
Response modulation by stimulus repetition
The ITC is the last purely visual area in the ventral visual
pathway, and its neurons extract information about shapes,
colors and even categories of objects (e.g. faces; Gross,
2008). An interesting phenomenon in monkey ITC is called
‘repetition suppression’: when the presentation of a novel
stimulus is repeated several times, ITC neurons decrease the
strength of their response (Li et al. 1993). Repetition suppression is stimulus specific, so these neurons are not just
‘novelty detectors’. However, the effect in ITC shows greater
stimulus selectivity than the responses themselves (Sawamura et al. 2006), which might be due to a reflection of selectivity at the input rather than at the output level of a
neuron. Repetition suppression is a common feature of neurons not just in ITC but also in medial temporal and prefrontal cortices (Ranganath & Rainer, 2003).
In humans, repetition suppression has been studied using
non-invasive techniques (see Grill-Spector et al. 2006 for a
review), such as fMRI (Sayres & Grill-Spector, 2006) and scalp
€ ller, 2005). In particular, an fMRI study
EEG (Gruber & Mu
during a continuous recognition task with four presentations of test items showed that MTL structures exhibited a
decrease in their activity for old vs. new items (Johnson
et al. 2008). Moreover, it was also found that this effect can
be graded, with continuing reductions after each successive
presentation. However, repetition suppression in humans
has been seen mostly at the category level (e.g. faces or
scenes), failing to achieve the selective type of responses
found in monkey ITC. One exception is given by a recent
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
Human medial temporal lobe, H. G. Rey et al. 11 1
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period (i.e. between 200 and 600 ms after stimulus onset) it
should have a median number of spikes (across trials) larger
than 1, to rule out neurons with too few spikes, and larger
than lb + 4rb, where lb and rb are the mean and standard
deviation (across all stimuli) of the median number of spikes
in the baseline period (between 600 and 200 ms). Second, it should have an average instantaneous firing rate
(calculated by convolving the spike train with a Gaussian
kernel) crossing over a threshold T for at least 100 ms (with
the upwards and downwards crossings defining the spiking
response onset and offset, respectively). The threshold T
was set to lFR + 4rFR, where lFR and rFR are the mean and
standard deviation (across all stimuli) of the instantaneous
firing rate during the baseline window (with a minimum at
5 Hz, for neurons with low baseline firing). Given that these
MTL responses have a very well-defined onset (Mormann
et al. 2008), this condition was introduced to avoid falsepositive detections due to random increases spread over
time. Third, to assure that the neuron’s response was consistent across trials, we compared the single-trial spike counts
in the post-stimulus (200 ms, 600 ms) and baseline ( 600
ms, 200 ms) windows with a paired sign test and required
a significant difference with P < 0.01.
Using this criterion, we identified a total of 72 responses
(eight from amygdala and 64 from hippocampus), coming
from 45 different units in 35 different channels. To illustrate
the experimental and data processing approach detailed
above, in Fig. 7 we show the responses of a unit in the left
hippocampus that selectively fired to the picture of the Taj
Mahal. The top panel shows the raster plot for the screening session (six trials), and in the bottom panel the response
of the same neuron obtained 5 h later during a following
testing session, using 25 repetitions of a subset of 14 pictures. Both in the screening and the testing sessions we
observe a similar response to the picture of the Taj Mahal.
Sample
Amplitude (µV)
Fig. 7 Response criterion. Example of a unit
in the left hippocampus responsive to the
picture of the Taj Mahal. The top panel
shows the results from the first screening
session. On the left, the waveforms of the
3520 spikes (recorded in 28 min) are
superimposed. On the right, the raster plot
associated to the picture of the Taj Mahal is
shown (in blue). The red curve corresponds to
the time profile of the mean firing rate
(obtained by convolving the spike train with a
Gaussian kernel with a value of r = 10 ms),
whereas the vertical dashed lines mark the
crossing of a threshold (magenta line), and
mark the onset and offset of the response.
The bottom panel shows the equivalent
results from (putatively) the same neuron
(2112 spikes in 16 min), but during the
testing session (5 h after the first one).
Amplitude (µV)
study with human single-cell recordings, in which the
responses of several screening sessions (as the ones
described in the Experimental paradigm section) were analyzed (Pedreira et al. 2010). In this study only the very first
screening sessions were considered, so the stimuli used were
completely novel in the first trial. The activity of these neurons decayed with trial number, resembling the repetition
suppression effect found in monkeys, and it was argued
that this pattern of responses may reflect the decrease of
relevant information to be stored into memory after each
presentation (Pedreira et al. 2010).
The screening data used in the study of Pedreira and colleagues had only six presentations of the stimuli. Although
a response modulation with stimulus repetition was evident, the stability of these representations over several trials
could not be addressed, as it was hard to see a steady state
in the response strength. Therefore, the question of
whether the response would end up reaching a baseline
level after a sufficient number of repetitions remained
open. In order to study the stability of the representations
by concept cells, we performed ‘testing sessions’, in which a
subset of 10–25 pictures from the screening session (including all the ones that elicited a response) were used, but
each of these images was shown 25–35 times in pseudorandom order (Fig. 2, bottom). We studied recordings during
13 testing sessions in four patients (all right-handed, three
male, 23–49 years old). From the 441 channels analyzed in
this study, 37% showed spiking activity from 457 units
(either multi- or single-unit).
After spikes were detected and sorted, it was necessary to
quantitatively assess whether a particular unit responded to
a particular picture. For this, we defined a responsiveness
criterion that balanced the trade-off between false-positives
and misses. According to our criterion, a responsive unit
should fulfill the following conditions. First, in the response
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12 Human medial temporal lobe, H. G. Rey et al.
If D is the duration of a response (time between onset
and offset of the response), we defined a baseline window
from max( 1000, 200 5*D) to 200, and a post-stimulus
window between the response onset and min(onset + 5*D,
2000), where the times are measured in milliseconds from
stimulus onset. The spike count on both windows was normalized for each response by the maximum (across trials)
count in the post-stimulus window. As a first point in the
analysis of these responses, to verify that repetition suppression effects were not due to varying levels of awareness,
attention, etc. during the recording session, we checked
that the normalized spike count in the baseline period
showed no differences across trials (Kruskal–Wallis test, P =
a
0.6). Then, we computed the difference between the normalized spike count in post-stimulus and baseline windows
(Fig. 8a). When looking at the first six trials, we observe a
repetition suppression effect similar to the one reported in
previous studies with stimulus-selective neurons (Li et al.
1993; Pedreira et al. 2010). For example, the decay of the
response is well fitted by an exponential function. However,
the larger number of repetitions used here lets us unveil
that the response has a clear non-zero steady state, which is
reached after ~10 trials (as in Sawamura et al. 2006). In
other words, although after several repetitions the response
strength was smaller than in the first trial, it remained well
above baseline even after about 30 repetitions, with about
0.5
Mean spike count
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and baseline windows as a function of the trial number (number of times a particular stimulus was presented). The mean (dots) and SEM (error
bars) were computed across the 72 responses found in the data set. The mean values were also fitted with an exponential model (solid line). (b)
Time course of the mean normalized firing rate (across the 72 responses) for a group of selected trials. In the left panel, the time courses of each
response for trial i are aligned to the stimulus onset before averaging, whereas in the right panel they are aligned to the response onset. For the
computation of the instantaneous firing rate we used the Gaussian kernel with a value of r = 50 ms.
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
Human medial temporal lobe, H. G. Rey et al. 13 1
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15 intervening stimuli on average. Although we cannot rule
out a further decrease after many more repetitions, it seems
that concept cells continue responding to a stimulus above
baseline. This relatively stable representation resembles the
findings with place cells in rodents, as it has been shown
that, unless the environment is changed, place cells can
maintain the same tuning properties for months (Thompson & Best, 1990).
To investigate the repetition suppression effect in more
detail, we analyzed the time profile of the neural responses
across trials. Figure 8b shows the averaged normalized firing rate for a selection of trials at the different stages of
the experimental sessions. A similar analysis was performed
in Pedreira et al. (2010) and Li et al. (1993), but only using
the first six repetitions. In line with these studies, we found
a similar response onset for the different trials (Fig. 8b, left),
with responses starting to deviate from baseline at about
250 ms after stimulus onset. The right plot of Fig. 8b shows
the same results but aligning the instantaneous firing rate
curves of each response to their corresponding response
onsets. Confirming the findings of a previous study with a
different dataset (Pedreira et al. 2010), we observe a
decrease in the peak latency with trial number, which is
related to a shortening of the response duration. There is
also a large decay in peak amplitude, which was much less
pronounced in Pedreira et al. (2010) as in that case only the
first six trials were considered. Moreover, we observe no
clear differences in the time profile of the responses after
trial 15.
In summary, we showed that MTL neurons have a stable
representation. In particular, the screening sessions used in
Pedreira et al. (2010) were the very first experiments done
with the patients, so the first trial corresponds to the very
first presentation of each stimulus. Given the strong
responses found from the first trial, we can infer that concept cells were involved in the encoding of the concept
(which had not necessarily been active in the recent past)
before the experiment took place. In fact, a non-stable,
temporary representation of concepts would have implied
a reassignment of these concepts to random sets of neurons
after the first presentation, which would have taken at least
a few trials to establish. Following these arguments, we can
hypothesize that the stable representation by concept cells
might last for as long as the concept remains relevant. The
loss of relevance may then diminish the size of the cell
assembly encoding the concept, and this may constitute a
key neural mechanism of forgetting (Quian Quiroga,
2012a).
Concluding remarks
In this work we have described the main procedures to
obtain single-cell recordings from the human MTL. We have
stressed the importance of using optimal experimental
designs (with screening sessions) and data processing
methodologies (particularly spike sorting) in order to identify the activity of sparsely firing neurons. The evidence
reviewed above suggests that the activity of neurons in this
area is related to the conscious processing of stimuli perceived by the subjects. The activation of a neuron associated
to a certain concept is invariant to transformations of the
stimuli (and even independent of the sensory modality),
selective and explicit. In addition, concept cells tend to represent personally relevant concepts, as expected from their
proposed role in episodic memory. Finally, we have presented a characterization of the stability with stimulus repetition of responses by concept cells, showing a decrease in
response strength similar to the one described in high-level
visual areas in monkeys. However, the response strength
reached an asymptotic value larger than baseline firing,
even after more than 20 presentations. Altogether, we postulate that the exploitation of data obtained from intracranial recordings (performed for clinical reasons) and,
particularly, the further study of concept cells, can unveil
key mechanisms implemented in the brain for the storage
and retrieval of episodic memories.
Acknowledgements
The authors thank all patients for their participation and the
King’s College Hospital staff for technical assistance. The authors
acknowledge support from the National Institute for Health
Research (NIHR) Biomedical Research Centre at the South London
and Maudsley Hospital. This work was supported by grants from
the Medical Research Council (G0701038, G0902089, G1002100)
and Engineering and Physical Sciences Research Council (EP/
H051651/1). Mark Richardson was funded by the Getty Family
Foundation.
References
Babb TL, Carr E, Crandall PH (1973) Analysis of extracellular firing patterns of deep temporal lobe structures in man. Electroencephalogr Clin Neurophysiol 34, 247–257.
Burwell RD, Agster KL (2008) 3.03 – Anatomy of the hippocampus and the declarative memory system. In: Learning and
Memory: A Comprehensive Reference. (ed. John HB), pp. 47–
66. Oxford: Academic Press.
Cerf M, Thiruvengadam N, Mormann F, et al. (2010) On-line,
voluntary control of human temporal lobe neurons. Nature
467, 1104–1108.
Eichenbaum H (2001) The hippocampus and declarative memory: cognitive mechanisms and neural codes. Behav Brain Res
127, 199–207.
Eichenbaum H (2004) Hippocampus: cognitive processes and
neural representations that underlie declarative memory. Neuron 44, 109–120.
Eichenbaum H, Dudchenko P, Wood E, et al. (1999) The hippocampus, memory, and place cells: is it spatial memory or a
memory space? Neuron 23, 209–226.
Engel AK, Moll CKE, Fried I, et al. (2005) Invasive recordings
from the human brain: clinical insights and beyond. Nat Rev
Neurosci 6, 35–47.
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
14 Human medial temporal lobe, H. G. Rey et al.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
Freiwald WA, Tsao DY (2010) Functional compartmentalization
and viewpoint generalization within the macaque face-processing system. Science 330, 845–851.
Fried I, MacDonald KA, Wilson CL (1997) Single neuron activity
in human hippocampus and amygdala during recognition of
faces and objects. Neuron 18, 753–765.
Gelbard-Sagiv H, Mukamel R, Harel M, et al. (2008) Internally
generated reactivation of single neurons in human hippocampus during free recall. Science 322, 96–101.
Grill-Spector K, Henson R, Martin A (2006) Repetition and the
brain: neural models of stimulus-specific effects. Trends Cogn
Sci 10, 14–23.
Gross CG (2008) Single neuron studies of inferior temporal cortex. Neuropsychologia 46, 841–852.
€ ller MM (2005) Oscillatory brain activity dissociates
Gruber T, Mu
between associative stimulus content in a repetition priming
task in the human EEG. Cereb Cortex 15, 109–116.
Halgren E, Babb TL, Crandall PH (1978) Activity of human hippocampal formation and amygdala neurons during memory testing. Electroencephalogr Clin Neurophysiol 45, 585–601.
Hassabis D, Kumaran D, Vann SD, et al. (2007) Patients with
hippocampal amnesia cannot imagine new experiences. Proc
Natl Acad Sci USA 104, 1726–1731.
Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex.
J Physiol 160, 106–154.
Hung CP, Kreiman G, Poggio T, et al. (2005) Fast readout of
object identity from macaque inferior temporal cortex. Science 310, 863–866.
Ison MJ, Quian Quiroga R, Fried I (2010) Fast remapping of single neuron responses in the human medial temporal lobe. In:
4 Soc. Neurosci. Abstr., 279.5. San Diego, CA, USA: ????.
Ison MJ, Mormann F, Cerf M, et al. (2011) Selectivity of pyramidal cells and interneurons in the human medial temporal
lobe. J Neurophysiol 106, 1713–1721.
Johnson JD, Muftuler LT, Rugg MD (2008) Multiple repetitions
reveal functionally and anatomically distinct patterns of hippocampal activity during continuous recognition memory.
Hippocampus 18, 975–980.
Kraskov A, Quian Quiroga R, Reddy L, et al. (2007) Local field
potentials and spikes in the human medial temporal lobe
are selective to image category. J Cogn Neurosci 19, 479–
492.
Kreiman G, Koch C, Fried I (2000a) Category-specific visual
responses of single neurons in the human medial temporal
lobe. Nat Neurosci 3, 946–953.
Kreiman G, Koch C, Fried I (2000b) Imagery neurons in the
human brain. Nature 408, 357–361.
Kreiman G, Fried I, Koch C (2002) Single-neuron correlates of
subjective vision in the human medial temporal lobe. Proc
Natl Acad Sci USA 99, 8378–8383.
Kreiman G, Hung CP, Kraskov A, et al. (2006) Object selectivity
of local field potentials and spikes in the macaque inferior
5 temporal cortex. Neuron 49, 433–445.
Kwan P, Schachter SC, Brodie MJ (2011) Drug-resistant epilepsy.
N Engl J Med 365, 919–926.
Li L, Miller EK, Desimone R (1993) The representation of stimulus familiarity in anterior inferior temporal cortex. J Neurophysiol 69, 1918–1929.
Liu H, Agam Y, Madsen JR, et al. (2009) Timing, timing, timing:
fast decoding of object information from intracranial field
potentials in human visual cortex. Neuron 62, 281–290.
1
Logothetis NK, Sheinberg DL (1996) Visual object recognition.
Annu Rev Neurosci 19, 577–621.
Martinez J, Pedreira C, Ison MJ, et al. (2009) Realistic simulation
of extracellular recordings. J Neurosci Methods 184, 285–293.
Mormann F, Kornblith S, Quian Quiroga R, et al. (2008) Latency
and selectivity of single neurons indicate hierarchical processing in the human medial temporal lobe. J Neurosci 28, 8865–
8872.
Mormann F, Dubois J, Kornblith S, et al. (2011) A category-specific response to animals in the right human amygdala. Nat
Neurosci 14, 1247–1249.
Moscovitch M, Rosenbaum RS, Gilboa A, et al. (2005) Functional
neuroanatomy of remote episodic, semantic and spatial memory: a unified account based on multiple trace theory. J Anat
207, 35–66.
Mukamel R, Fried I (2012) Human intracranial recordings and
cognitive neuroscience. Annu Rev Psychol 63, 511–537.
Niedermeyer E (1993) Epileptic seizure disorders. In:Electroencephalography: Basic Principles, Clinical Applications, and
6
Related Fields, pp. 476–585. ????: ????.
Ojemann GA, Creutzfeldt O, Lettich E, et al. (1988) Neuronal
activity in human lateral temporal cortex related to short-term
verbal memory, naming and reading. Brain 111(Pt 6), 1383–
1403.
Ojemann GA, Ojemann SG, Fried I (1998) Lessons from the
human brain: neuronal activity related to cognition. Neuroscientist 4, 285–300.
O’keefe J, Nadel L (1978) The Hippocampus as a Cognitive Map.
Oxford: Clarendon Press.
Pedreira C, Mormann F, Kraskov A, et al. (2010) Responses of
human medial temporal lobe neurons are modulated by stimulus repetition. J Neurophysiol 103, 97–107.
Penfield W (1975) The Mystery of the Mind: a Critical Study of
Consciousness and the Human Brain. ????: Princeton University
7
Press.
Quian Quiroga R (2009) What is the real shape of extracellular
spikes? J Neurosci Methods 177, 194–198.
Quian Quiroga R (2012a) Concept cells: the building blocks of
declarative memory functions. Nat Rev Neurosci 13, 587–597.
Quian Quiroga R (2012b) Quick guide: spike sorting. Curr Biol
22, R45–R46.
Quian Quiroga R, Panzeri S (2009) Extracting information from
neuronal populations: information theory and decoding
approaches. Nat Rev Neurosci 10, 173–185.
Quian Quiroga R, Nadasdy Z, Ben-Shaul Y (2004) Unsupervised
spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput 16, 1661–1687.
Quian Quiroga R, Reddy L, Kreiman G, et al. (2005) Invariant
visual representation by single neurons in the human brain.
Nature 435, 1102–1107.
Quian Quiroga R, Reddy L, Koch C, et al. (2007) Decoding visual
inputs from multiple neurons in the human temporal lobe.
J Neurophysiol 98, 1997–2007.
Quian Quiroga R, Kreiman G, Koch C, et al. (2008a) Sparse but
not ‘grandmother-cell’ coding in the medial temporal lobe.
Trends Cogn Sci 12, 87–91.
Quian Quiroga R, Mukamel R, Isham EA, et al. (2008b) Human
single-neuron responses at the threshold of conscious recognition. Proc Natl Acad Sci USA 105, 3599–3604.
Quian Quiroga R, Kraskov A, Koch C, et al. (2009) Explicit
encoding of multimodal percepts by single neurons in the
human brain. Curr Biol 19, 1308–1313.
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society
Human medial temporal lobe, H. G. Rey et al. 15 1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
Quian Quiroga R, Fried I, Koch C (2013) Brain cells for grandmother. Sci Am 308, 30–35.
Quirk GJ, Muller RU, Kubie JL (1990) The firing of hippocampal
place cells in the dark depends on the rat’s recent experience.
J Neurosci 10, 2008–2017.
Ranganath C, Rainer G (2003) Neural mechanisms for detecting
and remembering novel events. Nat Rev Neurosci 4, 193–202.
Reddy L, Quian Quiroga R, Wilken P, et al. (2006) A single-neuron correlate of change detection and change blindness in
the human medial temporal lobe. Curr Biol 16, 2066–2072.
Redish AD, Battaglia FP, Chawla MK, et al. (2001) Independence
of firing correlates of anatomically proximate hippocampal
pyramidal cells. J Neurosci 21, RC134.
Rey HG, Fried I, Quian Quiroga R (2014) Timing of single-neuron
and local field potential responses in the human medial temporal lobe. Curr Biol 24, 299–304.
Rutishauser U, Ross IB, Mamelak AN, et al. (2010) Human memory strength is predicted by theta-frequency phase-locking of
single neurons. Nature 464, 903–907.
Saleem KS, Tanaka K (1996) Divergent projections from the anterior inferotemporal area TE to the perirhinal and entorhinal
cortices in the macaque monkey. J Neurosci 16, 4757–4775.
Sawamura H, Orban GA, Vogels R (2006) Selectivity of neuronal
adaptation does not match response selectivity: a single-cell
study of the FMRI adaptation paradigm. Neuron 49, 307–318.
Sayres R, Grill-Spector K (2006) Object-selective cortex exhibits
performance-independent repetition suppression. J Neurophysiol 95, 995–1007.
€ ders HO (2008) Intractable epilepsy: management
Schuele SU, Lu
and therapeutic alternatives. Lancet Neurol 7, 514–524.
Squire LR, Zola-Morgan S (1991) The medial temporal lobe
memory system. Science 253, 1380–1386.
Squire LR, Stark CEL, Clark RE (2004) The medial temporal lobe*.
Annu Rev Neurosci 27, 279–306.
Suzuki WA (1996) Neuroanatomy of the monkey entorhinal,
perirhinal and parahippocampal cortices: organization of cortical inputs and interconnections with amygdala and striatum.
Semin Neurosci 8, 3–12.
Tanaka K (1996) Inferotemporal cortex and object vision. Annu
Rev Neurosci 19, 109–139.
Thompson LT, Best PJ (1990) Long-term stability of the placefield activity of single units recorded from the dorsal hippocampus of freely behaving rats. Brain Res 509, 299–308.
Thorpe SJ, Fabre-Thorpe M (2001) Seeking categories in the
8
brain. Science ????, 260–262.
Tsao DY, Livingstone MS (2008) Mechanisms of face perception.
Annu Rev Neurosci 31, 411–437.
Vanrullen R, Thorpe SJ (2001) The time course of visual processing: from early perception to decision-making. J Cogn Neurosci 13, 454–461.
Viskontas IV, Quian Quiroga R, Fried I (2009) Human medial
temporal lobe neurons respond preferentially to personally
relevant images. Proc Natl Acad Sci USA 106, 21 329–21 334.
Ward AA, Thomas LB (1955) The electrical activity of single units
in the cerebral cortex of man. Electroencephalogr Clin Neurophysiol 7, 135–136.
Waydo S, Koch C (2008) Unsupervised learning of individuals
and categories from images. Neural Comput 20, 1165–1178.
Waydo S, Kraskov A, Quian Quiroga R, et al. (2006) Sparse representation in the human medial temporal lobe. J Neurosci
26, 10 232–10 234.
Wilson MA, McNaughton BL (1993) Dynamics of the hippocampal ensemble code for space. Science 261, 1055–1058.
© 2014 2014 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society